Overview

Dataset statistics

Number of variables31
Number of observations72413
Missing cells139581
Missing cells (%)6.2%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory17.1 MiB
Average record size in memory248.0 B

Variable types

Numeric15
Categorical16

Alerts

inning has constant value "2"Constant
is_super_over has constant value "0"Constant
Dataset has 3 (< 0.1%) duplicate rowsDuplicates
batsman has a high cardinality: 445 distinct valuesHigh cardinality
non_striker has a high cardinality: 439 distinct valuesHigh cardinality
bowler has a high cardinality: 346 distinct valuesHigh cardinality
fielder has a high cardinality: 390 distinct valuesHigh cardinality
total_runs_x is highly overall correlated with city and 2 other fieldsHigh correlation
over is highly overall correlated with curr_score and 3 other fieldsHigh correlation
wide_runs is highly overall correlated with extra_runs and 1 other fieldsHigh correlation
legbye_runs is highly overall correlated with extra_runsHigh correlation
batsman_runs is highly overall correlated with total_runs_y and 1 other fieldsHigh correlation
extra_runs is highly overall correlated with wide_runs and 4 other fieldsHigh correlation
total_runs_y is highly overall correlated with wide_runs and 3 other fieldsHigh correlation
curr_score is highly overall correlated with over and 4 other fieldsHigh correlation
runs_left is highly overall correlated with total_runs_x and 4 other fieldsHigh correlation
ball_left is highly overall correlated with over and 3 other fieldsHigh correlation
wickets is highly overall correlated with over and 3 other fieldsHigh correlation
req_rr is highly overall correlated with total_runs_xHigh correlation
result is highly overall correlated with total_runs_xHigh correlation
player_dismissed is highly overall correlated with dismissal_kindHigh correlation
penalty_runs is highly overall correlated with extra_runsHigh correlation
bye_runs is highly overall correlated with extra_runsHigh correlation
dismissal_kind is highly overall correlated with batsman_runs and 1 other fieldsHigh correlation
match_id is highly overall correlated with cityHigh correlation
city is highly overall correlated with match_id and 4 other fieldsHigh correlation
winner is highly overall correlated with city and 2 other fieldsHigh correlation
batting_team is highly overall correlated with city and 1 other fieldsHigh correlation
bowling_team is highly overall correlated with city and 1 other fieldsHigh correlation
curr_rr is highly overall correlated with curr_scoreHigh correlation
city has 832 (1.1%) missing valuesMissing
dismissal_kind has 68860 (95.1%) missing valuesMissing
fielder has 69855 (96.5%) missing valuesMissing
wide_runs has 70186 (96.9%) zerosZeros
legbye_runs has 71246 (98.4%) zerosZeros
batsman_runs has 28989 (40.0%) zerosZeros
extra_runs has 68564 (94.7%) zerosZeros
total_runs_y has 25895 (35.8%) zerosZeros
curr_score has 818 (1.1%) zerosZeros
curr_rr has 818 (1.1%) zerosZeros

Reproduction

Analysis started2022-12-14 02:04:43.092793
Analysis finished2022-12-14 02:06:14.224999
Duration1 minute and 31.13 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

match_id
Real number (ℝ)

Distinct625
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2081.5187
Minimum1
Maximum11415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-14T07:36:14.431565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile62
Q1192
median396
Q3569
95-th percentile11320
Maximum11415
Range11414
Interquartile range (IQR)377

Descriptive statistics

Standard deviation3724.7717
Coefficient of variation (CV)1.789449
Kurtosis1.1923904
Mean2081.5187
Median Absolute Deviation (MAD)191
Skewness1.7220164
Sum1.5072902 × 108
Variance13873925
MonotonicityIncreasing
2022-12-14T07:36:14.703828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
570 134
 
0.2%
534 134
 
0.2%
553 131
 
0.2%
7909 130
 
0.2%
7924 130
 
0.2%
126 130
 
0.2%
211 129
 
0.2%
7905 129
 
0.2%
257 129
 
0.2%
502 129
 
0.2%
Other values (615) 71108
98.2%
ValueCountFrequency (%)
1 123
0.2%
5 124
0.2%
7 126
0.2%
8 89
0.1%
10 113
0.2%
11 104
0.1%
12 119
0.2%
14 124
0.2%
15 121
0.2%
18 123
0.2%
ValueCountFrequency (%)
11415 124
0.2%
11414 116
0.2%
11413 124
0.2%
11412 113
0.2%
11347 100
0.1%
11346 114
0.2%
11345 123
0.2%
11344 98
0.1%
11343 113
0.2%
11342 122
0.2%

city
Categorical

HIGH CORRELATION
MISSING

Distinct29
Distinct (%)< 0.1%
Missing832
Missing (%)1.1%
Memory size1.1 MiB
Mumbai
9911 
Kolkata
7805 
Delhi
7280 
Hyderabad
6523 
Chennai
6317 
Other values (24)
33745 

Length

Max length14
Median length13
Mean length7.4843604
Min length4

Characters and Unicode

Total characters535738
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHyderabad
2nd rowHyderabad
3rd rowHyderabad
4th rowHyderabad
5th rowHyderabad

Common Values

ValueCountFrequency (%)
Mumbai 9911
13.7%
Kolkata 7805
10.8%
Delhi 7280
10.1%
Hyderabad 6523
9.0%
Chennai 6317
8.7%
Bangalore 5927
 
8.2%
Jaipur 4835
 
6.7%
Chandigarh 4572
 
6.3%
Durban 1725
 
2.4%
Bengaluru 1610
 
2.2%
Other values (19) 15076
20.8%

Length

2022-12-14T07:36:14.951564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mumbai 9911
13.4%
kolkata 7805
10.5%
delhi 7280
9.8%
hyderabad 6523
 
8.8%
chennai 6317
 
8.5%
bangalore 5927
 
8.0%
jaipur 4835
 
6.5%
chandigarh 4572
 
6.2%
durban 1725
 
2.3%
bengaluru 1610
 
2.2%
Other values (23) 17677
23.8%

Most occurring characters

ValueCountFrequency (%)
a 93669
17.5%
i 39889
 
7.4%
n 36410
 
6.8%
e 35889
 
6.7%
h 32050
 
6.0%
r 31843
 
5.9%
l 26282
 
4.9%
u 25654
 
4.8%
b 23219
 
4.3%
d 21344
 
4.0%
Other values (31) 169489
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 458955
85.7%
Uppercase Letter 74182
 
13.8%
Space Separator 2601
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 93669
20.4%
i 39889
8.7%
n 36410
 
7.9%
e 35889
 
7.8%
h 32050
 
7.0%
r 31843
 
6.9%
l 26282
 
5.7%
u 25654
 
5.6%
b 23219
 
5.1%
d 21344
 
4.7%
Other values (13) 92706
20.2%
Uppercase Letter
ValueCountFrequency (%)
C 13555
18.3%
M 11113
15.0%
D 10825
14.6%
K 8163
11.0%
B 7786
10.5%
H 6523
8.8%
J 5780
7.8%
A 2174
 
2.9%
P 2128
 
2.9%
R 1277
 
1.7%
Other values (7) 4858
 
6.5%
Space Separator
ValueCountFrequency (%)
2601
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 533137
99.5%
Common 2601
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 93669
17.6%
i 39889
 
7.5%
n 36410
 
6.8%
e 35889
 
6.7%
h 32050
 
6.0%
r 31843
 
6.0%
l 26282
 
4.9%
u 25654
 
4.8%
b 23219
 
4.4%
d 21344
 
4.0%
Other values (30) 166888
31.3%
Common
ValueCountFrequency (%)
2601
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 535738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 93669
17.5%
i 39889
 
7.4%
n 36410
 
6.8%
e 35889
 
6.7%
h 32050
 
6.0%
r 31843
 
5.9%
l 26282
 
4.9%
u 25654
 
4.8%
b 23219
 
4.3%
d 21344
 
4.0%
Other values (31) 169489
31.6%

winner
Categorical

Distinct10
Distinct (%)< 0.1%
Missing27
Missing (%)< 0.1%
Memory size1.1 MiB
Mumbai Indians
11681 
Chennai Super Kings
10955 
Kolkata Knight Riders
9338 
Kings XI Punjab
8390 
Rajasthan Royals
8086 
Other values (5)
23936 

Length

Max length27
Median length19
Mean length18.004946
Min length14

Characters and Unicode

Total characters1303306
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunrisers Hyderabad
2nd rowSunrisers Hyderabad
3rd rowSunrisers Hyderabad
4th rowSunrisers Hyderabad
5th rowSunrisers Hyderabad

Common Values

ValueCountFrequency (%)
Mumbai Indians 11681
16.1%
Chennai Super Kings 10955
15.1%
Kolkata Knight Riders 9338
12.9%
Kings XI Punjab 8390
11.6%
Rajasthan Royals 8086
11.2%
Royal Challengers Bangalore 7792
10.8%
Delhi Daredevils 6363
8.8%
Sunrisers Hyderabad 5645
7.8%
Deccan Chargers 2952
 
4.1%
Delhi Capitals 1184
 
1.6%
(Missing) 27
 
< 0.1%

Length

2022-12-14T07:36:15.185884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T07:36:15.509134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
kings 19345
 
10.7%
mumbai 11681
 
6.4%
indians 11681
 
6.4%
chennai 10955
 
6.0%
super 10955
 
6.0%
kolkata 9338
 
5.2%
knight 9338
 
5.2%
riders 9338
 
5.2%
xi 8390
 
4.6%
punjab 8390
 
4.6%
Other values (12) 71836
39.6%

Most occurring characters

ValueCountFrequency (%)
a 150820
 
11.6%
n 114612
 
8.8%
108861
 
8.4%
i 93077
 
7.1%
e 92091
 
7.1%
s 86117
 
6.6%
r 65079
 
5.0%
l 63686
 
4.9%
g 47219
 
3.6%
h 46670
 
3.6%
Other values (23) 435074
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1004808
77.1%
Uppercase Letter 189637
 
14.6%
Space Separator 108861
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 150820
15.0%
n 114612
11.4%
i 93077
9.3%
e 92091
9.2%
s 86117
8.6%
r 65079
 
6.5%
l 63686
 
6.3%
g 47219
 
4.7%
h 46670
 
4.6%
d 38672
 
3.8%
Other values (11) 206765
20.6%
Uppercase Letter
ValueCountFrequency (%)
K 38021
20.0%
R 33302
17.6%
C 22883
12.1%
I 20071
10.6%
D 16862
8.9%
S 16600
8.8%
M 11681
 
6.2%
P 8390
 
4.4%
X 8390
 
4.4%
B 7792
 
4.1%
Space Separator
ValueCountFrequency (%)
108861
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1194445
91.6%
Common 108861
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 150820
 
12.6%
n 114612
 
9.6%
i 93077
 
7.8%
e 92091
 
7.7%
s 86117
 
7.2%
r 65079
 
5.4%
l 63686
 
5.3%
g 47219
 
4.0%
h 46670
 
3.9%
d 38672
 
3.2%
Other values (22) 396402
33.2%
Common
ValueCountFrequency (%)
108861
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1303306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 150820
 
11.6%
n 114612
 
8.8%
108861
 
8.4%
i 93077
 
7.1%
e 92091
 
7.1%
s 86117
 
6.6%
r 65079
 
5.0%
l 63686
 
4.9%
g 47219
 
3.6%
h 46670
 
3.6%
Other values (23) 435074
33.4%

total_runs_x
Real number (ℝ)

Distinct142
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.58396
Minimum65
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-14T07:36:15.877403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile116
Q1146
median165
Q3185
95-th percentile213
Maximum250
Range185
Interquartile range (IQR)39

Descriptive statistics

Standard deviation29.2822
Coefficient of variation (CV)0.17684201
Kurtosis0.30068104
Mean165.58396
Median Absolute Deviation (MAD)19
Skewness-0.10985878
Sum11990431
Variance857.44726
MonotonicityNot monotonic
2022-12-14T07:36:16.132837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165 2071
 
2.9%
164 1563
 
2.2%
168 1463
 
2.0%
171 1462
 
2.0%
183 1445
 
2.0%
148 1273
 
1.8%
157 1229
 
1.7%
178 1196
 
1.7%
170 1188
 
1.6%
187 1187
 
1.6%
Other values (132) 58336
80.6%
ValueCountFrequency (%)
65 20
 
< 0.1%
67 87
0.1%
70 80
0.1%
74 110
0.2%
80 86
0.1%
81 89
0.1%
82 84
0.1%
92 166
0.2%
93 51
 
0.1%
94 110
0.2%
ValueCountFrequency (%)
250 126
 
0.2%
246 125
 
0.2%
241 120
 
0.2%
240 124
 
0.2%
235 123
 
0.2%
233 123
 
0.2%
232 233
0.3%
231 369
0.5%
230 250
0.3%
227 125
 
0.2%

inning
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2
72413 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72413
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 72413
100.0%

Length

2022-12-14T07:36:16.387165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T07:36:16.591036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2 72413
100.0%

Most occurring characters

ValueCountFrequency (%)
2 72413
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 72413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 72413
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 72413
100.0%

batting_team
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Royal Challengers Bangalore
9684 
Kolkata Knight Riders
9658 
Mumbai Indians
9063 
Delhi Daredevils
9034 
Kings XI Punjab
8701 
Other values (5)
26273 

Length

Max length27
Median length19
Mean length18.220872
Min length14

Characters and Unicode

Total characters1319428
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowRoyal Challengers Bangalore
3rd rowRoyal Challengers Bangalore
4th rowRoyal Challengers Bangalore
5th rowRoyal Challengers Bangalore

Common Values

ValueCountFrequency (%)
Royal Challengers Bangalore 9684
13.4%
Kolkata Knight Riders 9658
13.3%
Mumbai Indians 9063
12.5%
Delhi Daredevils 9034
12.5%
Kings XI Punjab 8701
12.0%
Rajasthan Royals 8543
11.8%
Chennai Super Kings 8487
11.7%
Sunrisers Hyderabad 4445
6.1%
Deccan Chargers 3633
 
5.0%
Delhi Capitals 1165
 
1.6%

Length

2022-12-14T07:36:16.784246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T07:36:17.078804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
kings 17188
 
9.5%
delhi 10199
 
5.6%
royal 9684
 
5.3%
bangalore 9684
 
5.3%
challengers 9684
 
5.3%
kolkata 9658
 
5.3%
knight 9658
 
5.3%
riders 9658
 
5.3%
mumbai 9063
 
5.0%
indians 9063
 
5.0%
Other values (12) 77817
42.9%

Most occurring characters

ValueCountFrequency (%)
a 155058
 
11.8%
108943
 
8.3%
n 106636
 
8.1%
e 100107
 
7.6%
i 87960
 
6.7%
s 85401
 
6.5%
l 77335
 
5.9%
r 67148
 
5.1%
h 50204
 
3.8%
g 49847
 
3.8%
Other values (23) 430789
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1020428
77.3%
Uppercase Letter 190057
 
14.4%
Space Separator 108943
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 155058
15.2%
n 106636
10.5%
e 100107
9.8%
i 87960
8.6%
s 85401
8.4%
l 77335
 
7.6%
r 67148
 
6.6%
h 50204
 
4.9%
g 49847
 
4.9%
o 37569
 
3.7%
Other values (11) 203163
19.9%
Uppercase Letter
ValueCountFrequency (%)
K 36504
19.2%
R 36428
19.2%
C 22969
12.1%
D 22866
12.0%
I 17764
9.3%
S 12932
 
6.8%
B 9684
 
5.1%
M 9063
 
4.8%
X 8701
 
4.6%
P 8701
 
4.6%
Space Separator
ValueCountFrequency (%)
108943
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1210485
91.7%
Common 108943
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 155058
 
12.8%
n 106636
 
8.8%
e 100107
 
8.3%
i 87960
 
7.3%
s 85401
 
7.1%
l 77335
 
6.4%
r 67148
 
5.5%
h 50204
 
4.1%
g 49847
 
4.1%
o 37569
 
3.1%
Other values (22) 393220
32.5%
Common
ValueCountFrequency (%)
108943
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1319428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 155058
 
11.8%
108943
 
8.3%
n 106636
 
8.1%
e 100107
 
7.6%
i 87960
 
6.7%
s 85401
 
6.5%
l 77335
 
5.9%
r 67148
 
5.1%
h 50204
 
3.8%
g 49847
 
3.8%
Other values (23) 430789
32.6%

bowling_team
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Mumbai Indians
10807 
Chennai Super Kings
9456 
Kings XI Punjab
9387 
Royal Challengers Bangalore
8537 
Kolkata Knight Riders
8268 
Other values (5)
25958 

Length

Max length27
Median length19
Mean length18.018615
Min length14

Characters and Unicode

Total characters1304782
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunrisers Hyderabad
2nd rowSunrisers Hyderabad
3rd rowSunrisers Hyderabad
4th rowSunrisers Hyderabad
5th rowSunrisers Hyderabad

Common Values

ValueCountFrequency (%)
Mumbai Indians 10807
14.9%
Chennai Super Kings 9456
13.1%
Kings XI Punjab 9387
13.0%
Royal Challengers Bangalore 8537
11.8%
Kolkata Knight Riders 8268
11.4%
Rajasthan Royals 7251
10.0%
Delhi Daredevils 6883
9.5%
Sunrisers Hyderabad 6524
9.0%
Deccan Chargers 4588
6.3%
Delhi Capitals 712
 
1.0%

Length

2022-12-14T07:36:17.427336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T07:36:17.731428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
kings 18843
 
10.4%
mumbai 10807
 
6.0%
indians 10807
 
6.0%
chennai 9456
 
5.2%
super 9456
 
5.2%
xi 9387
 
5.2%
punjab 9387
 
5.2%
royal 8537
 
4.7%
challengers 8537
 
4.7%
bangalore 8537
 
4.7%
Other values (12) 76720
42.5%

Most occurring characters

ValueCountFrequency (%)
a 150676
 
11.5%
n 112461
 
8.6%
108061
 
8.3%
e 96376
 
7.4%
i 88163
 
6.8%
s 86188
 
6.6%
r 70429
 
5.4%
l 64857
 
5.0%
g 48773
 
3.7%
h 45695
 
3.5%
Other values (23) 433103
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1006860
77.2%
Uppercase Letter 189861
 
14.6%
Space Separator 108061
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 150676
15.0%
n 112461
11.2%
e 96376
9.6%
i 88163
8.8%
s 86188
8.6%
r 70429
 
7.0%
l 64857
 
6.4%
g 48773
 
4.8%
h 45695
 
4.5%
d 39006
 
3.9%
Other values (11) 204236
20.3%
Uppercase Letter
ValueCountFrequency (%)
K 35379
18.6%
R 31307
16.5%
C 23293
12.3%
I 20194
10.6%
D 19066
10.0%
S 15980
8.4%
M 10807
 
5.7%
P 9387
 
4.9%
X 9387
 
4.9%
B 8537
 
4.5%
Space Separator
ValueCountFrequency (%)
108061
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1196721
91.7%
Common 108061
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 150676
 
12.6%
n 112461
 
9.4%
e 96376
 
8.1%
i 88163
 
7.4%
s 86188
 
7.2%
r 70429
 
5.9%
l 64857
 
5.4%
g 48773
 
4.1%
h 45695
 
3.8%
d 39006
 
3.3%
Other values (22) 394097
32.9%
Common
ValueCountFrequency (%)
108061
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1304782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 150676
 
11.5%
n 112461
 
8.6%
108061
 
8.3%
e 96376
 
7.4%
i 88163
 
6.8%
s 86188
 
6.6%
r 70429
 
5.4%
l 64857
 
5.0%
g 48773
 
3.7%
h 45695
 
3.5%
Other values (23) 433103
33.2%

over
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9557262
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-14T07:36:18.060668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.5638665
Coefficient of variation (CV)0.55886094
Kurtosis-1.1589334
Mean9.9557262
Median Absolute Deviation (MAD)5
Skewness0.065215267
Sum720924
Variance30.95661
MonotonicityNot monotonic
2022-12-14T07:36:18.273029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 3929
 
5.4%
2 3924
 
5.4%
3 3900
 
5.4%
4 3872
 
5.3%
6 3860
 
5.3%
8 3858
 
5.3%
5 3857
 
5.3%
7 3851
 
5.3%
9 3837
 
5.3%
10 3811
 
5.3%
Other values (10) 33714
46.6%
ValueCountFrequency (%)
1 3929
5.4%
2 3924
5.4%
3 3900
5.4%
4 3872
5.3%
5 3857
5.3%
6 3860
5.3%
7 3851
5.3%
8 3858
5.3%
9 3837
5.3%
10 3811
5.3%
ValueCountFrequency (%)
20 1943
2.7%
19 2818
3.9%
18 3227
4.5%
17 3450
4.8%
16 3554
4.9%
15 3645
5.0%
14 3726
5.1%
13 3783
5.2%
12 3782
5.2%
11 3786
5.2%

ball
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6085648
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-14T07:36:18.490052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8065665
Coefficient of variation (CV)0.50063296
Kurtosis-1.0866746
Mean3.6085648
Median Absolute Deviation (MAD)2
Skewness0.099522406
Sum261307
Variance3.2636824
MonotonicityNot monotonic
2022-12-14T07:36:18.662632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 11813
16.3%
2 11751
16.2%
3 11694
16.1%
4 11642
16.1%
5 11573
16.0%
6 11507
15.9%
7 2071
 
2.9%
8 320
 
0.4%
9 42
 
0.1%
ValueCountFrequency (%)
1 11813
16.3%
2 11751
16.2%
3 11694
16.1%
4 11642
16.1%
5 11573
16.0%
6 11507
15.9%
7 2071
 
2.9%
8 320
 
0.4%
9 42
 
0.1%
ValueCountFrequency (%)
9 42
 
0.1%
8 320
 
0.4%
7 2071
 
2.9%
6 11507
15.9%
5 11573
16.0%
4 11642
16.1%
3 11694
16.1%
2 11751
16.2%
1 11813
16.3%

batsman
Categorical

Distinct445
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
G Gambhir
 
1877
V Kohli
 
1745
RG Sharma
 
1499
RV Uthappa
 
1479
S Dhawan
 
1422
Other values (440)
64391 

Length

Max length17
Median length16
Mean length9.2996838
Min length5

Characters and Unicode

Total characters673418
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowCH Gayle
2nd rowMandeep Singh
3rd rowMandeep Singh
4th rowMandeep Singh
5th rowMandeep Singh

Common Values

ValueCountFrequency (%)
G Gambhir 1877
 
2.6%
V Kohli 1745
 
2.4%
RG Sharma 1499
 
2.1%
RV Uthappa 1479
 
2.0%
S Dhawan 1422
 
2.0%
JH Kallis 1357
 
1.9%
AT Rayudu 1332
 
1.8%
SK Raina 1293
 
1.8%
SR Watson 1241
 
1.7%
DA Warner 1230
 
1.7%
Other values (435) 57938
80.0%

Length

2022-12-14T07:36:18.897088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v 2646
 
1.8%
s 2592
 
1.7%
sr 2168
 
1.5%
da 1907
 
1.3%
g 1877
 
1.3%
gambhir 1877
 
1.3%
singh 1872
 
1.3%
sharma 1840
 
1.2%
de 1821
 
1.2%
r 1774
 
1.2%
Other values (602) 128215
86.3%

Most occurring characters

ValueCountFrequency (%)
76176
 
11.3%
a 74932
 
11.1%
i 32503
 
4.8%
n 30444
 
4.5%
h 29880
 
4.4%
r 29477
 
4.4%
e 27563
 
4.1%
S 26711
 
4.0%
l 26258
 
3.9%
s 18343
 
2.7%
Other values (43) 301131
44.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 392086
58.2%
Uppercase Letter 205084
30.5%
Space Separator 76176
 
11.3%
Dash Punctuation 72
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 74932
19.1%
i 32503
 
8.3%
n 30444
 
7.8%
h 29880
 
7.6%
r 29477
 
7.5%
e 27563
 
7.0%
l 26258
 
6.7%
s 18343
 
4.7%
o 15679
 
4.0%
t 15275
 
3.9%
Other values (16) 91732
23.4%
Uppercase Letter
ValueCountFrequency (%)
S 26711
13.0%
R 18305
 
8.9%
K 17009
 
8.3%
A 16423
 
8.0%
M 16220
 
7.9%
P 14251
 
6.9%
D 13153
 
6.4%
G 10515
 
5.1%
V 9791
 
4.8%
J 9771
 
4.8%
Other values (15) 52935
25.8%
Space Separator
ValueCountFrequency (%)
76176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 597170
88.7%
Common 76248
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 74932
 
12.5%
i 32503
 
5.4%
n 30444
 
5.1%
h 29880
 
5.0%
r 29477
 
4.9%
e 27563
 
4.6%
S 26711
 
4.5%
l 26258
 
4.4%
s 18343
 
3.1%
R 18305
 
3.1%
Other values (41) 282754
47.3%
Common
ValueCountFrequency (%)
76176
99.9%
- 72
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 673418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
76176
 
11.3%
a 74932
 
11.1%
i 32503
 
4.8%
n 30444
 
4.5%
h 29880
 
4.4%
r 29477
 
4.4%
e 27563
 
4.1%
S 26711
 
4.0%
l 26258
 
3.9%
s 18343
 
2.7%
Other values (43) 301131
44.7%

non_striker
Categorical

Distinct439
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
G Gambhir
 
2002
RG Sharma
 
1604
V Kohli
 
1578
S Dhawan
 
1477
RV Uthappa
 
1428
Other values (434)
64324 

Length

Max length17
Median length16
Mean length9.2902932
Min length5

Characters and Unicode

Total characters672738
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowMandeep Singh
2nd rowCH Gayle
3rd rowCH Gayle
4th rowCH Gayle
5th rowCH Gayle

Common Values

ValueCountFrequency (%)
G Gambhir 2002
 
2.8%
RG Sharma 1604
 
2.2%
V Kohli 1578
 
2.2%
S Dhawan 1477
 
2.0%
RV Uthappa 1428
 
2.0%
AT Rayudu 1386
 
1.9%
SK Raina 1358
 
1.9%
JH Kallis 1310
 
1.8%
AM Rahane 1306
 
1.8%
CH Gayle 1242
 
1.7%
Other values (429) 57722
79.7%

Length

2022-12-14T07:36:19.119599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s 2642
 
1.8%
v 2600
 
1.8%
sr 2312
 
1.6%
sharma 2014
 
1.4%
g 2002
 
1.3%
gambhir 2002
 
1.3%
r 1774
 
1.2%
de 1757
 
1.2%
da 1756
 
1.2%
m 1711
 
1.2%
Other values (599) 127930
86.1%

Most occurring characters

ValueCountFrequency (%)
76087
 
11.3%
a 75819
 
11.3%
i 31963
 
4.8%
n 30339
 
4.5%
h 29628
 
4.4%
r 29552
 
4.4%
e 27842
 
4.1%
S 26908
 
4.0%
l 25649
 
3.8%
R 18709
 
2.8%
Other values (43) 300242
44.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 391244
58.2%
Uppercase Letter 205338
30.5%
Space Separator 76087
 
11.3%
Dash Punctuation 69
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 75819
19.4%
i 31963
 
8.2%
n 30339
 
7.8%
h 29628
 
7.6%
r 29552
 
7.6%
e 27842
 
7.1%
l 25649
 
6.6%
s 17921
 
4.6%
o 15011
 
3.8%
d 14911
 
3.8%
Other values (16) 92609
23.7%
Uppercase Letter
ValueCountFrequency (%)
S 26908
13.1%
R 18709
 
9.1%
K 16881
 
8.2%
M 16477
 
8.0%
A 16288
 
7.9%
P 14194
 
6.9%
D 12624
 
6.1%
G 10836
 
5.3%
V 9772
 
4.8%
J 9667
 
4.7%
Other values (15) 52982
25.8%
Space Separator
ValueCountFrequency (%)
76087
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 596582
88.7%
Common 76156
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 75819
 
12.7%
i 31963
 
5.4%
n 30339
 
5.1%
h 29628
 
5.0%
r 29552
 
5.0%
e 27842
 
4.7%
S 26908
 
4.5%
l 25649
 
4.3%
R 18709
 
3.1%
s 17921
 
3.0%
Other values (41) 282252
47.3%
Common
ValueCountFrequency (%)
76087
99.9%
- 69
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 672738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
76087
 
11.3%
a 75819
 
11.3%
i 31963
 
4.8%
n 30339
 
4.5%
h 29628
 
4.4%
r 29552
 
4.4%
e 27842
 
4.1%
S 26908
 
4.0%
l 25649
 
3.8%
R 18709
 
2.8%
Other values (43) 300242
44.6%

bowler
Categorical

Distinct346
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Harbhajan Singh
 
1622
A Mishra
 
1506
SL Malinga
 
1416
R Ashwin
 
1364
PP Chawla
 
1344
Other values (341)
65161 

Length

Max length17
Median length15
Mean length9.493903
Min length5

Characters and Unicode

Total characters687482
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowA Nehra
2nd rowA Nehra
3rd rowA Nehra
4th rowA Nehra
5th rowA Nehra

Common Values

ValueCountFrequency (%)
Harbhajan Singh 1622
 
2.2%
A Mishra 1506
 
2.1%
SL Malinga 1416
 
2.0%
R Ashwin 1364
 
1.9%
PP Chawla 1344
 
1.9%
DW Steyn 1077
 
1.5%
B Kumar 1070
 
1.5%
DJ Bravo 1053
 
1.5%
P Kumar 1002
 
1.4%
I Sharma 989
 
1.4%
Other values (336) 59970
82.8%

Length

2022-12-14T07:36:19.337379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sharma 4063
 
2.8%
singh 3839
 
2.6%
r 3800
 
2.6%
a 3643
 
2.5%
kumar 2938
 
2.0%
m 2598
 
1.8%
s 2571
 
1.7%
pp 2326
 
1.6%
p 2184
 
1.5%
sk 1674
 
1.1%
Other values (488) 117709
79.9%

Most occurring characters

ValueCountFrequency (%)
a 89013
 
12.9%
74932
 
10.9%
n 36907
 
5.4%
h 36569
 
5.3%
r 36188
 
5.3%
i 30536
 
4.4%
e 27893
 
4.1%
S 27634
 
4.0%
l 22112
 
3.2%
M 19202
 
2.8%
Other values (42) 286496
41.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 420479
61.2%
Uppercase Letter 191861
27.9%
Space Separator 74932
 
10.9%
Dash Punctuation 210
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 89013
21.2%
n 36907
 
8.8%
h 36569
 
8.7%
r 36188
 
8.6%
i 30536
 
7.3%
e 27893
 
6.6%
l 22112
 
5.3%
o 16336
 
3.9%
t 16194
 
3.9%
m 15585
 
3.7%
Other values (16) 93146
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 27634
14.4%
M 19202
10.0%
A 17154
 
8.9%
P 16388
 
8.5%
K 14000
 
7.3%
R 13115
 
6.8%
J 12247
 
6.4%
B 10613
 
5.5%
D 8472
 
4.4%
C 7768
 
4.0%
Other values (14) 45268
23.6%
Space Separator
ValueCountFrequency (%)
74932
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 612340
89.1%
Common 75142
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 89013
 
14.5%
n 36907
 
6.0%
h 36569
 
6.0%
r 36188
 
5.9%
i 30536
 
5.0%
e 27893
 
4.6%
S 27634
 
4.5%
l 22112
 
3.6%
M 19202
 
3.1%
A 17154
 
2.8%
Other values (40) 269132
44.0%
Common
ValueCountFrequency (%)
74932
99.7%
- 210
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 687482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 89013
 
12.9%
74932
 
10.9%
n 36907
 
5.4%
h 36569
 
5.3%
r 36188
 
5.3%
i 30536
 
4.4%
e 27893
 
4.1%
S 27634
 
4.0%
l 22112
 
3.2%
M 19202
 
2.8%
Other values (42) 286496
41.7%

is_super_over
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
72413 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72413
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72413
100.0%

Length

2022-12-14T07:36:19.572522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T07:36:19.878668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 72413
100.0%

Most occurring characters

ValueCountFrequency (%)
0 72413
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72413
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72413
100.0%

wide_runs
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.036692307
Minimum0
Maximum5
Zeros70186
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-14T07:36:20.030305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24530921
Coefficient of variation (CV)6.6855761
Kurtosis187.54845
Mean0.036692307
Median Absolute Deviation (MAD)0
Skewness11.385234
Sum2657
Variance0.060176608
MonotonicityNot monotonic
2022-12-14T07:36:20.215104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 70186
96.9%
1 2042
 
2.8%
2 90
 
0.1%
5 74
 
0.1%
3 19
 
< 0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
0 70186
96.9%
1 2042
 
2.8%
2 90
 
0.1%
3 19
 
< 0.1%
4 2
 
< 0.1%
5 74
 
0.1%
ValueCountFrequency (%)
5 74
 
0.1%
4 2
 
< 0.1%
3 19
 
< 0.1%
2 90
 
0.1%
1 2042
 
2.8%
0 70186
96.9%

bye_runs
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
72233 
1
 
111
4
 
58
2
 
10
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72413
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72233
99.8%
1 111
 
0.2%
4 58
 
0.1%
2 10
 
< 0.1%
3 1
 
< 0.1%

Length

2022-12-14T07:36:20.431377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T07:36:20.699872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 72233
99.8%
1 111
 
0.2%
4 58
 
0.1%
2 10
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 72233
99.8%
1 111
 
0.2%
4 58
 
0.1%
2 10
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72233
99.8%
1 111
 
0.2%
4 58
 
0.1%
2 10
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 72413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72233
99.8%
1 111
 
0.2%
4 58
 
0.1%
2 10
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72233
99.8%
1 111
 
0.2%
4 58
 
0.1%
2 10
 
< 0.1%
3 1
 
< 0.1%

legbye_runs
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0212669
Minimum0
Maximum5
Zeros71246
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-14T07:36:20.892050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.199127
Coefficient of variation (CV)9.3632361
Kurtosis241.88224
Mean0.0212669
Median Absolute Deviation (MAD)0
Skewness13.873947
Sum1540
Variance0.039651563
MonotonicityNot monotonic
2022-12-14T07:36:21.079093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 71246
98.4%
1 1000
 
1.4%
4 97
 
0.1%
2 62
 
0.1%
3 6
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 71246
98.4%
1 1000
 
1.4%
2 62
 
0.1%
3 6
 
< 0.1%
4 97
 
0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 97
 
0.1%
3 6
 
< 0.1%
2 62
 
0.1%
1 1000
 
1.4%
0 71246
98.4%

noball_runs
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
72139 
1
 
267
5
 
5
2
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72413
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72139
99.6%
1 267
 
0.4%
5 5
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

Length

2022-12-14T07:36:21.288730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T07:36:22.223500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 72139
99.6%
1 267
 
0.4%
5 5
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 72139
99.6%
1 267
 
0.4%
5 5
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72139
99.6%
1 267
 
0.4%
5 5
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 72413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72139
99.6%
1 267
 
0.4%
5 5
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72139
99.6%
1 267
 
0.4%
5 5
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

penalty_runs
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
72411 
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72411
> 99.9%
5 2
 
< 0.1%

Length

2022-12-14T07:36:22.419956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T07:36:22.634715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 72411
> 99.9%
5 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 72411
> 99.9%
5 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72411
> 99.9%
5 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 72413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72411
> 99.9%
5 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72411
> 99.9%
5 2
 
< 0.1%

batsman_runs
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2362145
Minimum0
Maximum7
Zeros28989
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-14T07:36:22.796372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6035255
Coefficient of variation (CV)1.2971257
Kurtosis1.6549782
Mean1.2362145
Median Absolute Deviation (MAD)1
Skewness1.5887674
Sum89518
Variance2.5712941
MonotonicityNot monotonic
2022-12-14T07:36:22.962191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 28989
40.0%
1 27123
37.5%
4 8250
 
11.4%
2 4539
 
6.3%
6 3227
 
4.5%
3 240
 
0.3%
5 40
 
0.1%
7 5
 
< 0.1%
ValueCountFrequency (%)
0 28989
40.0%
1 27123
37.5%
2 4539
 
6.3%
3 240
 
0.3%
4 8250
 
11.4%
5 40
 
0.1%
6 3227
 
4.5%
7 5
 
< 0.1%
ValueCountFrequency (%)
7 5
 
< 0.1%
6 3227
 
4.5%
5 40
 
0.1%
4 8250
 
11.4%
3 240
 
0.3%
2 4539
 
6.3%
1 27123
37.5%
0 28989
40.0%

extra_runs
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.067253118
Minimum0
Maximum7
Zeros68564
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-14T07:36:23.155500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34451209
Coefficient of variation (CV)5.1226188
Kurtosis90.395879
Mean0.067253118
Median Absolute Deviation (MAD)0
Skewness8.2173049
Sum4870
Variance0.11868858
MonotonicityNot monotonic
2022-12-14T07:36:23.329036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 68564
94.7%
1 3420
 
4.7%
2 162
 
0.2%
4 157
 
0.2%
5 82
 
0.1%
3 27
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 68564
94.7%
1 3420
 
4.7%
2 162
 
0.2%
3 27
 
< 0.1%
4 157
 
0.2%
5 82
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 82
 
0.1%
4 157
 
0.2%
3 27
 
< 0.1%
2 162
 
0.2%
1 3420
 
4.7%
0 68564
94.7%

total_runs_y
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3034676
Minimum0
Maximum10
Zeros25895
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-14T07:36:23.532070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6033953
Coefficient of variation (CV)1.2300998
Kurtosis1.6917364
Mean1.3034676
Median Absolute Deviation (MAD)1
Skewness1.5660181
Sum94388
Variance2.5708764
MonotonicityNot monotonic
2022-12-14T07:36:23.715974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 29225
40.4%
0 25895
35.8%
4 8341
 
11.5%
2 5266
 
7.3%
6 3219
 
4.4%
3 280
 
0.4%
5 128
 
0.2%
8 33
 
< 0.1%
7 17
 
< 0.1%
10 9
 
< 0.1%
ValueCountFrequency (%)
0 25895
35.8%
1 29225
40.4%
2 5266
 
7.3%
3 280
 
0.4%
4 8341
 
11.5%
5 128
 
0.2%
6 3219
 
4.4%
7 17
 
< 0.1%
8 33
 
< 0.1%
10 9
 
< 0.1%
ValueCountFrequency (%)
10 9
 
< 0.1%
8 33
 
< 0.1%
7 17
 
< 0.1%
6 3219
 
4.4%
5 128
 
0.2%
4 8341
 
11.5%
3 280
 
0.4%
2 5266
 
7.3%
1 29225
40.4%
0 25895
35.8%

player_dismissed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
68860 
1
 
3553

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68860
95.1%
1 3553
 
4.9%

Length

2022-12-14T07:36:23.928629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T07:36:24.135427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 68860
95.1%
1 3553
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 68860
95.1%
1 3553
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68860
95.1%
1 3553
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 72413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68860
95.1%
1 3553
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68860
95.1%
1 3553
 
4.9%

dismissal_kind
Categorical

HIGH CORRELATION
MISSING

Distinct9
Distinct (%)0.3%
Missing68860
Missing (%)95.1%
Memory size1.1 MiB
caught
2162 
bowled
660 
run out
292 
lbw
223 
stumped
 
120
Other values (4)
 
96

Length

Max length21
Median length6
Mean length6.209682
Min length3

Characters and Unicode

Total characters22063
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowbowled
2nd rowcaught
3rd rowrun out
4th rowcaught
5th rowcaught

Common Values

ValueCountFrequency (%)
caught 2162
 
3.0%
bowled 660
 
0.9%
run out 292
 
0.4%
lbw 223
 
0.3%
stumped 120
 
0.2%
caught and bowled 85
 
0.1%
retired hurt 6
 
< 0.1%
hit wicket 4
 
< 0.1%
obstructing the field 1
 
< 0.1%
(Missing) 68860
95.1%

Length

2022-12-14T07:36:24.328586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T07:36:24.611742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
caught 2247
55.8%
bowled 745
 
18.5%
run 292
 
7.3%
out 292
 
7.3%
lbw 223
 
5.5%
stumped 120
 
3.0%
and 85
 
2.1%
retired 6
 
0.1%
hurt 6
 
0.1%
hit 4
 
0.1%
Other values (4) 7
 
0.2%

Most occurring characters

ValueCountFrequency (%)
u 2958
13.4%
t 2682
12.2%
a 2332
10.6%
h 2258
10.2%
c 2252
10.2%
g 2248
10.2%
o 1038
 
4.7%
w 972
 
4.4%
b 969
 
4.4%
l 969
 
4.4%
Other values (11) 3385
15.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21589
97.9%
Space Separator 474
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 2958
13.7%
t 2682
12.4%
a 2332
10.8%
h 2258
10.5%
c 2252
10.4%
g 2248
10.4%
o 1038
 
4.8%
w 972
 
4.5%
b 969
 
4.5%
l 969
 
4.5%
Other values (10) 2911
13.5%
Space Separator
ValueCountFrequency (%)
474
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21589
97.9%
Common 474
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 2958
13.7%
t 2682
12.4%
a 2332
10.8%
h 2258
10.5%
c 2252
10.4%
g 2248
10.4%
o 1038
 
4.8%
w 972
 
4.5%
b 969
 
4.5%
l 969
 
4.5%
Other values (10) 2911
13.5%
Common
ValueCountFrequency (%)
474
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22063
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 2958
13.4%
t 2682
12.2%
a 2332
10.6%
h 2258
10.2%
c 2252
10.2%
g 2248
10.2%
o 1038
 
4.7%
w 972
 
4.4%
b 969
 
4.4%
l 969
 
4.4%
Other values (11) 3385
15.3%

fielder
Categorical

HIGH CARDINALITY
MISSING

Distinct390
Distinct (%)15.2%
Missing69855
Missing (%)96.5%
Memory size1.1 MiB
MS Dhoni
 
64
KD Karthik
 
59
SK Raina
 
48
WP Saha
 
44
AC Gilchrist
 
41
Other values (385)
2302 

Length

Max length21
Median length19
Mean length9.4655981
Min length5

Characters and Unicode

Total characters24213
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)3.9%

Sample

1st rowDA Warner
2nd rowBCJ Cutting
3rd rowYuvraj Singh
4th rowMC Henriques
5th rowYuvraj Singh

Common Values

ValueCountFrequency (%)
MS Dhoni 64
 
0.1%
KD Karthik 59
 
0.1%
SK Raina 48
 
0.1%
WP Saha 44
 
0.1%
AC Gilchrist 41
 
0.1%
KA Pollard 41
 
0.1%
RG Sharma 39
 
0.1%
NV Ojha 37
 
0.1%
DJ Bravo 37
 
0.1%
AB de Villiers 36
 
< 0.1%
Other values (380) 2112
 
2.9%
(Missing) 69855
96.5%

Length

2022-12-14T07:36:24.907105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s 82
 
1.5%
m 76
 
1.4%
r 75
 
1.4%
sharma 73
 
1.4%
dj 71
 
1.3%
ms 69
 
1.3%
patel 67
 
1.3%
karthik 65
 
1.2%
dhoni 64
 
1.2%
singh 62
 
1.2%
Other values (517) 4601
86.7%

Most occurring characters

ValueCountFrequency (%)
a 2758
 
11.4%
2747
 
11.3%
i 1240
 
5.1%
h 1160
 
4.8%
n 1080
 
4.5%
r 1068
 
4.4%
S 926
 
3.8%
e 904
 
3.7%
l 847
 
3.5%
K 641
 
2.6%
Other values (45) 10842
44.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14202
58.7%
Uppercase Letter 7168
29.6%
Space Separator 2747
 
11.3%
Open Punctuation 46
 
0.2%
Close Punctuation 46
 
0.2%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2758
19.4%
i 1240
 
8.7%
h 1160
 
8.2%
n 1080
 
7.6%
r 1068
 
7.5%
e 904
 
6.4%
l 847
 
6.0%
s 635
 
4.5%
t 597
 
4.2%
o 565
 
4.0%
Other values (16) 3348
23.6%
Uppercase Letter
ValueCountFrequency (%)
S 926
12.9%
K 641
 
8.9%
A 618
 
8.6%
M 584
 
8.1%
P 545
 
7.6%
R 539
 
7.5%
D 500
 
7.0%
J 370
 
5.2%
B 338
 
4.7%
V 283
 
3.9%
Other values (15) 1824
25.4%
Space Separator
ValueCountFrequency (%)
2747
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21370
88.3%
Common 2843
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2758
 
12.9%
i 1240
 
5.8%
h 1160
 
5.4%
n 1080
 
5.1%
r 1068
 
5.0%
S 926
 
4.3%
e 904
 
4.2%
l 847
 
4.0%
K 641
 
3.0%
s 635
 
3.0%
Other values (41) 10111
47.3%
Common
ValueCountFrequency (%)
2747
96.6%
( 46
 
1.6%
) 46
 
1.6%
- 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24213
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2758
 
11.4%
2747
 
11.3%
i 1240
 
5.1%
h 1160
 
4.8%
n 1080
 
4.5%
r 1068
 
4.4%
S 926
 
3.8%
e 904
 
3.7%
l 847
 
3.5%
K 641
 
2.6%
Other values (45) 10842
44.8%

curr_score
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct224
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.325577
Minimum0
Maximum223
Zeros818
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-14T07:36:25.164178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q134
median70
Q3109
95-th percentile155
Maximum223
Range223
Interquartile range (IQR)75

Descriptive statistics

Standard deviation46.906255
Coefficient of variation (CV)0.63969841
Kurtosis-0.73883557
Mean73.325577
Median Absolute Deviation (MAD)37
Skewness0.3279981
Sum5309725
Variance2200.1968
MonotonicityNot monotonic
2022-12-14T07:36:25.427719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 818
 
1.1%
1 815
 
1.1%
10 610
 
0.8%
4 610
 
0.8%
5 585
 
0.8%
60 584
 
0.8%
61 573
 
0.8%
6 567
 
0.8%
2 564
 
0.8%
68 547
 
0.8%
Other values (214) 66140
91.3%
ValueCountFrequency (%)
0 818
1.1%
1 815
1.1%
2 564
0.8%
3 390
0.5%
4 610
0.8%
5 585
0.8%
6 567
0.8%
7 535
0.7%
8 540
0.7%
9 527
0.7%
ValueCountFrequency (%)
223 2
 
< 0.1%
222 5
< 0.1%
221 5
< 0.1%
220 1
 
< 0.1%
219 2
 
< 0.1%
218 2
 
< 0.1%
217 3
< 0.1%
216 4
< 0.1%
215 2
 
< 0.1%
214 3
< 0.1%

runs_left
Real number (ℝ)

Distinct257
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.258379
Minimum-16
Maximum249
Zeros175
Zeros (%)0.2%
Negative389
Negative (%)0.5%
Memory size1.1 MiB
2022-12-14T07:36:25.711500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-16
5-th percentile13
Q153
median92
Q3130
95-th percentile175
Maximum249
Range265
Interquartile range (IQR)77

Descriptive statistics

Standard deviation50.021962
Coefficient of variation (CV)0.54219425
Kurtosis-0.73987709
Mean92.258379
Median Absolute Deviation (MAD)39
Skewness0.14488524
Sum6680706
Variance2502.1967
MonotonicityNot monotonic
2022-12-14T07:36:25.987454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112 547
 
0.8%
96 530
 
0.7%
105 528
 
0.7%
79 519
 
0.7%
103 516
 
0.7%
94 516
 
0.7%
98 509
 
0.7%
77 509
 
0.7%
76 509
 
0.7%
97 506
 
0.7%
Other values (247) 67224
92.8%
ValueCountFrequency (%)
-16 1
 
< 0.1%
-12 1
 
< 0.1%
-11 1
 
< 0.1%
-10 4
 
< 0.1%
-9 5
 
< 0.1%
-8 8
 
< 0.1%
-7 5
 
< 0.1%
-6 20
 
< 0.1%
-5 21
 
< 0.1%
-4 65
0.1%
ValueCountFrequency (%)
249 1
 
< 0.1%
248 1
 
< 0.1%
246 1
 
< 0.1%
242 2
 
< 0.1%
241 1
 
< 0.1%
240 1
 
< 0.1%
239 2
 
< 0.1%
236 6
< 0.1%
235 6
< 0.1%
234 1
 
< 0.1%

ball_left
Real number (ℝ)

Distinct122
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.657078
Minimum-2
Maximum119
Zeros241
Zeros (%)0.3%
Negative69
Negative (%)0.1%
Memory size1.1 MiB
2022-12-14T07:36:26.263592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile9
Q135
median63
Q392
95-th percentile114
Maximum119
Range121
Interquartile range (IQR)57

Descriptive statistics

Standard deviation33.404593
Coefficient of variation (CV)0.53313359
Kurtosis-1.1539417
Mean62.657078
Median Absolute Deviation (MAD)28
Skewness-0.064780509
Sum4537187
Variance1115.8668
MonotonicityNot monotonic
2022-12-14T07:36:26.548334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113 774
 
1.1%
107 769
 
1.1%
101 751
 
1.0%
95 737
 
1.0%
71 731
 
1.0%
83 730
 
1.0%
89 728
 
1.0%
65 725
 
1.0%
77 723
 
1.0%
59 706
 
1.0%
Other values (112) 65039
89.8%
ValueCountFrequency (%)
-2 9
 
< 0.1%
-1 60
 
0.1%
0 241
0.3%
1 276
0.4%
2 305
0.4%
3 327
0.5%
4 366
0.5%
5 473
0.7%
6 416
0.6%
7 429
0.6%
ValueCountFrequency (%)
119 625
0.9%
118 625
0.9%
117 625
0.9%
116 625
0.9%
115 625
0.9%
114 626
0.9%
113 774
1.1%
112 650
0.9%
111 628
0.9%
110 624
0.9%

wickets
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5359673
Minimum0
Maximum10
Zeros67
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size848.6 KiB
2022-12-14T07:36:26.791764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median8
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1386366
Coefficient of variation (CV)0.28379059
Kurtosis0.30093604
Mean7.5359673
Median Absolute Deviation (MAD)1
Skewness-0.89079641
Sum545702
Variance4.5737664
MonotonicityNot monotonic
2022-12-14T07:36:26.974363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9 14556
20.1%
10 14503
20.0%
8 12696
17.5%
7 10712
14.8%
6 7758
10.7%
5 4939
 
6.8%
4 3121
 
4.3%
3 2038
 
2.8%
2 1252
 
1.7%
1 771
 
1.1%
ValueCountFrequency (%)
0 67
 
0.1%
1 771
 
1.1%
2 1252
 
1.7%
3 2038
 
2.8%
4 3121
 
4.3%
5 4939
 
6.8%
6 7758
10.7%
7 10712
14.8%
8 12696
17.5%
9 14556
20.1%
ValueCountFrequency (%)
10 14503
20.0%
9 14556
20.1%
8 12696
17.5%
7 10712
14.8%
6 7758
10.7%
5 4939
 
6.8%
4 3121
 
4.3%
3 2038
 
2.8%
2 1252
 
1.7%
1 771
 
1.1%

curr_rr
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct5765
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4395235
Minimum0
Maximum42
Zeros818
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-14T07:36:27.219464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.8029268
Q16.2571429
median7.4805195
Q38.6823529
95-th percentile10.764706
Maximum42
Range42
Interquartile range (IQR)2.4252101

Descriptive statistics

Standard deviation2.2758499
Coefficient of variation (CV)0.3059134
Kurtosis7.2558193
Mean7.4395235
Median Absolute Deviation (MAD)1.2133581
Skewness0.44854234
Sum538718.21
Variance5.1794928
MonotonicityNot monotonic
2022-12-14T07:36:27.483168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2038
 
2.8%
0 818
 
1.1%
9 758
 
1.0%
8 757
 
1.0%
7.5 494
 
0.7%
3 490
 
0.7%
12 434
 
0.6%
10 373
 
0.5%
4 357
 
0.5%
7 354
 
0.5%
Other values (5755) 65540
90.5%
ValueCountFrequency (%)
0 818
1.1%
0.4285714286 1
 
< 0.1%
0.4615384615 2
 
< 0.1%
0.5 1
 
< 0.1%
0.5454545455 3
 
< 0.1%
0.6 4
 
< 0.1%
0.6666666667 9
 
< 0.1%
0.75 16
 
< 0.1%
0.8571428571 22
 
< 0.1%
0.9230769231 1
 
< 0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
36 3
 
< 0.1%
32 1
 
< 0.1%
30 8
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
25.2 1
 
< 0.1%
24 66
0.1%
22 1
 
< 0.1%
21 5
 
< 0.1%

req_rr
Real number (ℝ)

Distinct8997
Distinct (%)12.4%
Missing7
Missing (%)< 0.1%
Infinite234
Infinite (%)0.3%
Meannan
Minimum-inf
Maximuminf
Zeros168
Zeros (%)0.2%
Negative435
Negative (%)0.6%
Memory size1.1 MiB
2022-12-14T07:36:27.751405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-inf
5-th percentile3.8
Q17.1428571
median8.877551
Q310.909091
95-th percentile19.25
Maximuminf
Rangeinf
Interquartile range (IQR)3.7662338

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meannan
Median Absolute Deviation (MAD)1.8532182
Skewnessnan
Sumnan
Variancenan
MonotonicityNot monotonic
2022-12-14T07:36:28.016520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 663
 
0.9%
12 631
 
0.9%
9 572
 
0.8%
8 336
 
0.5%
10 300
 
0.4%
7.5 278
 
0.4%
18 241
 
0.3%
8.4 233
 
0.3%
9.6 219
 
0.3%
inf 216
 
0.3%
Other values (8987) 68717
94.9%
ValueCountFrequency (%)
-inf 18
< 0.1%
-510 1
 
< 0.1%
-462 1
 
< 0.1%
-372 1
 
< 0.1%
-348 1
 
< 0.1%
-330 1
 
< 0.1%
-288 1
 
< 0.1%
-270 1
 
< 0.1%
-264 2
 
< 0.1%
-222 1
 
< 0.1%
ValueCountFrequency (%)
inf 216
0.3%
678 1
 
< 0.1%
582 1
 
< 0.1%
546 1
 
< 0.1%
480 1
 
< 0.1%
468 1
 
< 0.1%
426 1
 
< 0.1%
396 1
 
< 0.1%
384 2
 
< 0.1%
378 1
 
< 0.1%

result
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
38062 
0
34351 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 38062
52.6%
0 34351
47.4%

Length

2022-12-14T07:36:28.260128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T07:36:28.475185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 38062
52.6%
0 34351
47.4%

Most occurring characters

ValueCountFrequency (%)
1 38062
52.6%
0 34351
47.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38062
52.6%
0 34351
47.4%

Most occurring scripts

ValueCountFrequency (%)
Common 72413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 38062
52.6%
0 34351
47.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 38062
52.6%
0 34351
47.4%

Interactions

2022-12-14T07:36:06.574505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:08.431761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:12.836539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:17.027728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:21.202861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:25.674905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:29.520294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:33.390175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:37.468217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:41.935429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:45.910201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:49.893213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:53.973013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:58.613753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:02.556764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:06.842347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:08.732177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:13.115628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:17.307274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:21.475783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:25.923409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:29.775104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:33.659699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:37.732250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:42.196647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:46.169322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:50.209231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:54.241706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:58.882767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:02.821779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:07.137436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:09.012116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:13.406306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:17.585130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:21.753721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:26.192685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:30.048486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:33.947600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:38.013073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:42.476762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:46.441550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:50.489576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:54.524326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:59.156988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:03.089888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:07.423745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:09.287255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:13.690585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:17.875735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:22.033980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:26.467434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:30.316791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:34.232112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:38.755433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:42.757171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:46.727459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:50.763056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:54.808269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:59.431099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:03.367749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:07.708173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:09.578205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:13.979631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:18.165890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:22.313300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:26.728604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:30.588547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:34.515845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:39.032130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:43.033738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:46.998401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:51.050895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:55.106788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:59.710475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:03.641116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:07.987616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:10.178890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:14.257593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:18.437176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:22.574288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:26.978589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:30.836704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:34.773783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:39.289329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:43.290870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:47.259007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:51.308122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:55.374430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:59.961397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:03.906790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:08.255231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:10.445395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:14.524434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:18.718139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:23.229403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:27.236746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:31.087507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:35.037664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:39.556700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:43.551533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:47.511130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:51.567882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:55.637099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:00.230689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:04.155943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:08.529554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:10.723088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:14.810411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:19.004488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:23.506736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:27.494055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:31.355419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:35.318979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:39.831008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:43.818305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:47.785676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:51.845677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:55.916290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:00.499993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:04.427797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:08.811995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:10.998403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:15.103901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:19.287936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:23.786190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:27.761242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:31.618647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:35.604453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:40.096374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:44.095322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:48.062064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:52.141797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:56.194081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:00.767447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:04.697944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:09.075152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:11.263881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:15.373642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:19.562412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:24.055805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:28.012130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:31.871067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:35.869590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:40.355419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:44.347875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:48.321151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:52.400348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:56.458267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:01.026301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:04.958740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:09.339004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:11.522890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:15.643428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:19.817888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:24.338951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:28.258829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:32.121703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:36.145578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:40.622654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:44.612444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:48.571838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:52.656815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:56.727034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:01.276096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:05.205907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:09.604356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:11.786556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:15.917513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:20.095231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:24.601851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:28.515989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:32.374394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:36.412945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:40.882985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:44.861439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:48.831395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:52.921241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:56.994820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:01.535145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:05.456243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:09.874386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:12.056002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:16.196805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:20.382883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:24.869474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:28.766798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:32.634627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:36.680093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:41.151198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:45.130888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:49.096811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:53.194192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:57.262365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:01.796492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:05.776324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:10.141906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:12.314352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:16.466657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:20.675307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:25.138558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:29.021382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:32.882001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:36.953062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:41.406032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:45.387788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:49.355474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:53.453488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:57.523237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:02.056142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:06.058171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:10.389781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:12.573321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:16.752440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:20.939706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:25.407214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:29.270252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:33.135918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:37.215653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:41.662342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:45.644904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:49.610060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:53.716802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:35:58.343120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:02.305360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-14T07:36:06.308984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-12-14T07:36:28.699380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-14T07:36:29.291808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-14T07:36:29.872674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-14T07:36:30.414377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-14T07:36:30.813231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-14T07:36:10.934391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-14T07:36:12.719765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-14T07:36:13.840284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

match_idcitywinnertotal_runs_xinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runs_yplayer_dismisseddismissal_kindfieldercurr_scoreruns_leftball_leftwicketscurr_rrreq_rrresult
1251HyderabadSunrisers Hyderabad2072Royal Challengers BangaloreSunrisers Hyderabad11CH GayleMandeep SinghA Nehra0000001010NaNNaN1206119106.00000010.3865550
1261HyderabadSunrisers Hyderabad2072Royal Challengers BangaloreSunrisers Hyderabad12Mandeep SinghCH GayleA Nehra0000000000NaNNaN1206118103.00000010.4745760
1271HyderabadSunrisers Hyderabad2072Royal Challengers BangaloreSunrisers Hyderabad13Mandeep SinghCH GayleA Nehra0000000000NaNNaN1206117102.00000010.5641030
1281HyderabadSunrisers Hyderabad2072Royal Challengers BangaloreSunrisers Hyderabad14Mandeep SinghCH GayleA Nehra0000002020NaNNaN3204116104.50000010.5517240
1291HyderabadSunrisers Hyderabad2072Royal Challengers BangaloreSunrisers Hyderabad15Mandeep SinghCH GayleA Nehra0000004040NaNNaN7200115108.40000010.4347830
1301HyderabadSunrisers Hyderabad2072Royal Challengers BangaloreSunrisers Hyderabad16Mandeep SinghCH GayleA Nehra0000004040NaNNaN111961141011.00000010.3157890
1311HyderabadSunrisers Hyderabad2072Royal Challengers BangaloreSunrisers Hyderabad21CH GayleMandeep SinghB Kumar0000000000NaNNaN11196113109.42857110.4070800
1321HyderabadSunrisers Hyderabad2072Royal Challengers BangaloreSunrisers Hyderabad22CH GayleMandeep SinghB Kumar0000000000NaNNaN11196112108.25000010.5000000
1331HyderabadSunrisers Hyderabad2072Royal Challengers BangaloreSunrisers Hyderabad23CH GayleMandeep SinghB Kumar0000001010NaNNaN12195111108.00000010.5405410
1341HyderabadSunrisers Hyderabad2072Royal Challengers BangaloreSunrisers Hyderabad24Mandeep SinghCH GayleB Kumar0000000000NaNNaN12195110107.20000010.6363640
match_idcitywinnertotal_runs_xinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runs_yplayer_dismisseddismissal_kindfieldercurr_scoreruns_leftball_leftwicketscurr_rrreq_rrresult
14956811415HyderabadMumbai Indians1522Chennai Super KingsMumbai Indians193RA JadejaSR WatsonJJ Bumrah0000002020NaNNaN14012957.5675688.0000000
14956911415HyderabadMumbai Indians1522Chennai Super KingsMumbai Indians194RA JadejaSR WatsonJJ Bumrah0000000000NaNNaN14012857.5000009.0000000
14957011415HyderabadMumbai Indians1522Chennai Super KingsMumbai Indians195RA JadejaSR WatsonJJ Bumrah0000002020NaNNaN14210757.5398238.5714290
14957111415HyderabadMumbai Indians1522Chennai Super KingsMumbai Indians196RA JadejaSR WatsonJJ Bumrah0040004480NaNNaN1502657.8947372.0000000
14957211415HyderabadMumbai Indians1522Chennai Super KingsMumbai Indians201SR WatsonRA JadejaSL Malinga0000001010NaNNaN1511557.8782611.2000000
14957311415HyderabadMumbai Indians1522Chennai Super KingsMumbai Indians202RA JadejaSR WatsonSL Malinga0000001010NaNNaN1520457.8620690.0000000
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14957511415HyderabadMumbai Indians1522Chennai Super KingsMumbai Indians204SR WatsonRA JadejaSL Malinga0000001011run outKH Pandya155-3247.881356-9.0000000
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14957711415HyderabadMumbai Indians1522Chennai Super KingsMumbai Indians206SN ThakurRA JadejaSL Malinga0000000001lbwNaN157-5037.850000-inf0

Duplicate rows

Most frequently occurring

match_idcitywinnertotal_runs_xinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runs_yplayer_dismisseddismissal_kindfieldercurr_scoreruns_leftball_leftwicketscurr_rrreq_rrresult# duplicates
07946JaipurRajasthan Royals1742Royal Challengers BangaloreRajasthan Royals101AB de VilliersMandeep SinghI Sodhi0000000000NaNNaN78966578.5090918.86153802
111150JaipurRajasthan Royals1642Rajasthan RoyalsRoyal Challengers Bangalore44JC ButtlerAM RahaneN Saini0000000000NaNNaN47117981012.8181827.16326512
211320KolkataChennai Super Kings1702Chennai Super KingsKolkata Knight Riders16SR WatsonF du PlessisP Krishna0000000000NaNNaN101601141010.0000008.42105312